NucTools: analysis of chromatin feature occupancy profiles from high-throughput sequencing data
Biomedical applications of high-throughput sequencing methods generate a vast amount of data in which numerous chromatin features are mapped along the genome. The results are frequently analysed by creating binary data sets that link the presence/absence of a given feature to specific genomic loci....
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
14 February 2017
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| In: |
BMC genomics
Year: 2017, Jahrgang: 18 |
| ISSN: | 1471-2164 |
| DOI: | 10.1186/s12864-017-3580-2 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1186/s12864-017-3580-2 Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12864-017-3580-2 
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| Verfasserangaben: | Yevhen Vainshtein, Karsten Rippe and Vladimir B. Teif |
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| 520 | |a Biomedical applications of high-throughput sequencing methods generate a vast amount of data in which numerous chromatin features are mapped along the genome. The results are frequently analysed by creating binary data sets that link the presence/absence of a given feature to specific genomic loci. However, the nucleosome occupancy or chromatin accessibility landscape is essentially continuous. It is currently a challenge in the field to cope with continuous distributions of deep sequencing chromatin readouts and to integrate the different types of discrete chromatin features to reveal linkages between them. | ||
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